diff --git a/quapy/evaluation.py b/quapy/evaluation.py
index 3fc3907..a270663 100644
--- a/quapy/evaluation.py
+++ b/quapy/evaluation.py
@@ -1,6 +1,6 @@
+import quapy as qp
 from typing import Union, Callable, Iterable
 from data import LabelledCollection
-from method.aggregative import AggregativeQuantifier, AggregativeProbabilisticQuantifier
 from method.base import BaseQuantifier
 from util import temp_seed
 import numpy as np
@@ -38,14 +38,18 @@ def artificial_sampling_prediction(
     with temp_seed(random_seed):
         indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, n_repetitions))
 
-    if isinstance(model, AggregativeQuantifier):
+    if isinstance(model, qp.method.aggregative.AggregativeQuantifier):
+        print('\tinstance of aggregative-quantifier')
         quantification_func = model.aggregate
-        if isinstance(model, AggregativeProbabilisticQuantifier):
+        if isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier):
+            print('\t\tinstance of probabilitstic-aggregative-quantifier')
             preclassified_instances = model.posterior_probabilities(test.instances)
         else:
+            print('\t\tinstance of hard-aggregative-quantifier')
             preclassified_instances = model.classify(test.instances)
         test = LabelledCollection(preclassified_instances, test.labels)
     else:
+        print('\t\tinstance of base-quantifier')
         quantification_func = model.quantify
 
     def _predict_prevalences(index):
diff --git a/tweet_sent_quant.py b/tweet_sent_quant.py
index 3965c0f..ec35010 100644
--- a/tweet_sent_quant.py
+++ b/tweet_sent_quant.py
@@ -44,9 +44,9 @@ def quantification_models():
     __C_range = np.logspace(-4, 5, 10)
     lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
     #yield 'cc', qp.method.aggregative.CC(newLR()), lr_params
-    yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
+    #yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
     #yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
-    #yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
+    yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
 
 
 def result_path(dataset_name, model_name, optim_metric):
@@ -69,7 +69,7 @@ if __name__ == '__main__':
 
     np.random.seed(0)
 
-    for dataset_name in ['hcr']:  # qp.datasets.TWITTER_SENTIMENT_DATASETS:
+    for dataset_name in ['sanders']:  # qp.datasets.TWITTER_SENTIMENT_DATASETS:
 
         benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
         benchmark_devel.stats()